There's plenty of other CSV parsers in the wild, but I had a hard time finding what I wanted. Inspired by Python's csv module, I wanted a library with simple, intuitive syntax. Furthermore, I wanted support for special use cases such as calculating statistics on very large files. Thus, this library was created with these following goals in mind.
A high performance CSV parser allows you to take advantage of the deluge of large datasets available. By using overlapped threads, memory mapped IO, and minimal memory allocation, this parser can quickly tackle large CSV files--even if they are larger than RAM.
In fact, according to Visual Studio's profier this CSV parser spends almost 90% of its CPU cycles actually reading your data as opposed to getting hung up in hard disk I/O or pushing around memory.
On my computer (12th Gen Intel(R) Core(TM) i5-12400 @ 2.50 GHz/Western Digital Blue 5400RPM HDD), this parser can read
- the 69.9 MB 2015_StateDepartment.csv in 0.19 seconds (360 MBps)
- a 1.4 GB Craigslist Used Vehicles Dataset in 1.18 seconds (1.2 GBps)
- a 2.9GB Car Accidents Dataset in 8.49 seconds (352 MBps)
This CSV parser is much more than a fancy string splitter, and parses all files following RFC 4180.
However, in reality we know that RFC 4180 is just a suggestion, and there's many "flavors" of CSV such as tab-delimited files. Thus, this library has:
- Automatic delimiter guessing
- Ability to ignore comments in leading rows and elsewhere
- Ability to handle rows of different lengths
- Ability to handle arbitrary line endings (as long as they are some combination of carriage return and newline)
By default, rows of variable length are silently ignored, although you may elect to keep them or throw an error.
This CSV parser is encoding-agnostic and will handle ANSI and UTF-8 encoded files. It does not try to decode UTF-8, except for detecting and stripping UTF-8 byte order marks.
This CSV parser has an extensive test suite and is checked for memory safety with Valgrind. If you still manage to find a bug, do not hesitate to report it.
In addition to the Features & Examples below, a fully-fledged online documentation contains more examples, details, interesting features, and instructions for less common use cases.
This library was developed with Microsoft Visual Studio and is compatible with >g++ 7.5 and clang.
All of the code required to build this library, aside from the C++ standard library, is contained under include/.
While C++17 is recommended, C++11 is the minimum version required. This library makes extensive use of string views, and uses
Martin Moene's string view library if std::string_view is not available.
This library is available as a single .hpp file under single_include/csv.hpp.
If you're including this in another CMake project, you can simply clone this repo into your project directory, and add the following to your CMakeLists.txt:
# Optional: Defaults to C++ 17
# set(CSV_CXX_STANDARD 11)
add_subdirectory(csv-parser)
# ...
add_executable(<your program> ...)
target_link_libraries(<your program> csv)
Don't want to clone? No problem. There's also a simple example documenting how to use CMake's FetchContent module to integrate this library.
With this library, you can easily stream over a large file without reading its entirety into memory.
C++ Style
# include "csv.hpp"
using namespace csv;
...
CSVReader reader("very_big_file.csv");
for (CSVRow& row: reader) { // Input iterator
for (CSVField& field: row) {
// By default, get<>() produces a std::string.
// A more efficient get<string_view>() is also available, where the resulting
// string_view is valid as long as the parent CSVRow is alive
std::cout << field.get<>() << ...
}
}
...Old-Fashioned C Style Loop
...
CSVReader reader("very_big_file.csv");
CSVRow row;
while (reader.read_row(row)) {
// Do stuff with row here
}
...By default, passing in a file path string to the constructor of CSVReader
causes memory-mapped IO to be used. In general, this option is the most
performant.
However, std::ifstream may also be used as well as in-memory sources via std::stringstream.
Note: Currently CSV guessing only works for memory-mapped files. The CSV dialect must be manually defined for other sources.
CSVFormat format;
// custom formatting options go here
CSVReader mmap("some_file.csv", format);
std::ifstream infile("some_file.csv", std::ios::binary);
CSVReader ifstream_reader(infile, format);
std::stringstream my_csv;
CSVReader sstream_reader(my_csv, format);Retrieving values using a column name string is a cheap, constant time operation.
# include "csv.hpp"
using namespace csv;
...
CSVReader reader("very_big_file.csv");
double sum = 0;
for (auto& row: reader) {
// Note: Can also use index of column with [] operator
sum += row["Total Salary"].get<double>();
}
...If your CSV has lots of numeric values, you can also have this parser (lazily) convert them to the proper data type.
- Type checking is performed on conversions to prevent undefined behavior and integer overflow
- Negative numbers cannot be blindly converted to unsigned integer types
get<float>(),get<double>(), andget<long double>()are capable of parsing numbers written in scientific notation.- Note: Conversions to floating point types are not currently checked for loss of precision.
# include "csv.hpp"
using namespace csv;
...
CSVReader reader("very_big_file.csv");
for (auto& row: reader) {
if (row["timestamp"].is_int()) {
// Can use get<>() with any integer type, but negative
// numbers cannot be converted to unsigned types
row["timestamp"].get<int>();
// You can also attempt to parse hex values
int value;
if (row["hexValue"].try_parse_hex(value)) {
std::cout << "Hex value is " << value << std::endl;
}
// Non-imperial decimal numbers can be handled this way
long double decimalValue;
if (row["decimalNumber"].try_parse_decimal(decimalValue, ',')) {
std::cout << "Decimal value is " << decimalValue << std::endl;
}
// ..
}
}
You can serialize individual rows as JSON objects, where the keys are column names, or as JSON arrays (which don't contain column names). The outputted JSON contains properly escaped strings with minimal whitespace and no quoting for numeric values. How these JSON fragments are assembled into a larger JSON document is an exercise left for the user.
# include <sstream>
# include "csv.hpp"
using namespace csv;
...
CSVReader reader("very_big_file.csv");
std::stringstream my_json;
for (auto& row: reader) {
my_json << row.to_json() << std::endl;
my_json << row.to_json_array() << std::endl;
// You can pass in a vector of column names to
// slice or rearrange the outputted JSON
my_json << row.to_json({ "A", "B", "C" }) << std::endl;
my_json << row.to_json_array({ "C", "B", "A" }) << std::endl;
}
Although the CSV parser has a decent guessing mechanism, in some cases it is preferrable to specify the exact parameters of a file.
# include "csv.hpp"
# include ...
using namespace csv;
CSVFormat format;
format.delimiter('\t')
.quote('~')
.header_row(2); // Header is on 3rd row (zero-indexed)
// .no_header(); // Parse CSVs without a header row
// .quote(false); // Turn off quoting
// Alternatively, we can use format.delimiter({ '\t', ',', ... })
// to tell the CSV guesser which delimiters to try out
CSVReader reader("wierd_csv_dialect.csv", format);
for (auto& row: reader) {
// Do stuff with rows here
}
This parser can efficiently trim off leading and trailing whitespace. Of course, make sure you don't include your intended delimiter or newlines in the list of characters to trim.
CSVFormat format;
format.trim({ ' ', '\t' });Sometimes, the rows in a CSV are not all of the same length. Whether this was intentional or not, this library is built to handle all use cases.
CSVFormat format;
// Default: Silently ignoring rows with missing or extraneous columns
format.variable_columns(false); // Short-hand
format.variable_columns(VariableColumnPolicy::IGNORE_ROW);
// Case 2: Keeping variable-length rows
format.variable_columns(true); // Short-hand
format.variable_columns(VariableColumnPolicy::KEEP);
// Case 3: Throwing an error if variable-length rows are encountered
format.variable_columns(VariableColumnPolicy::THROW);If a CSV file does not have column names, you can specify your own:
std::vector<std::string> col_names = { ... };
CSVFormat format;
format.column_names(col_names);# include "csv.hpp"
using namespace csv;
...
// Method 1: Using parse()
std::string csv_string = "Actor,Character\r\n"
"Will Ferrell,Ricky Bobby\r\n"
"John C. Reilly,Cal Naughton Jr.\r\n"
"Sacha Baron Cohen,Jean Giard\r\n";
auto rows = parse(csv_string);
for (auto& r: rows) {
// Do stuff with row here
}
// Method 2: Using _csv operator
auto rows = "Actor,Character\r\n"
"Will Ferrell,Ricky Bobby\r\n"
"John C. Reilly,Cal Naughton Jr.\r\n"
"Sacha Baron Cohen,Jean Giard\r\n"_csv;
for (auto& r: rows) {
// Do stuff with row here
}
# include "csv.hpp"
# include ...
using namespace csv;
using namespace std;
...
stringstream ss; // Can also use ofstream, etc.
auto writer = make_csv_writer(ss);
// auto writer = make_tsv_writer(ss); // For tab-separated files
// DelimWriter<stringstream, '|', '"'> writer(ss); // Your own custom format
// set_decimal_places(2); // How many places after the decimal will be written for floats
writer << vector<string>({ "A", "B", "C" })
<< deque<string>({ "I'm", "too", "tired" })
<< list<string>({ "to", "write", "documentation." });
writer << array<string, 2>({ "The quick brown", "fox", "jumps over the lazy dog" });
writer << make_tuple(1, 2.0, "Three");
...You can pass in arbitrary types into DelimWriter by defining a conversion function
for that type to std::string.
Bug reports, feature requests, and so on are always welcome. Feel free to leave a note in the Issues section.